I recently switched from Lisrel to Mplus. While I was rerunning some measurement models, I was suprised by large differences in the chi-square values that are reported by Mplus and Lisrel. Is there a simple explanation for this difference? (sorry if this question has been answered already...)

I think the difference is that you are using WLSMV in Mplus and WLSM in LISREL. The only value that is relevant for WLSMV is the p-value. The chi-square value and the degrees of freedom are not the regular statistics. The following paper discusses the Mplus estimators:

I think in both analyses wls was used (and thus not wlsm or wlsmv), because I specified this explicitely in the model (in mplus: 'estimator=wls', in Lisrel 'wls' as output option). But maybe I am doing something wrong.

My question is in the first place a practical one. The Lisrel fit indices suggest that the model is maybe not good but acceptable, the mplus indices completely reject the model. How do I decide which option is the correct one?

The Muthen et al paper (#75) that you requested describes how WLS performs poorly unless the model is very small and the sample very large. It shows that the Mplus WLSMV estimator works well. I would use WLSMV. In terms of fit indices I would largely rely on CFI. I, however, am more inclined to work with neighboring models, testing the model at hand against not the totally unrestricted model, but against a somewhat less restrictive model. This can be done in Mplus using DIFFEST (see the UG).

Dears Prof. Muthen, I run the example 3.11(Path analysis with continuous dependent variables) of your users guide in V6. Then I run the model on LISREL v 8.54(2003). Data, model specification, number of parameters, DF and method of estimation (ML) are same in two packages but the results is different in Chi2 value, RMSEA, parameters estimation and . . . !!